Since fluid flow phenomena contain highly nonlinear and chaotic nature, the techniques applied to the fluid flow data in the present study can be generalizable for the applications in a wide range of mechanical engineering.
In fact, a machine-learning-based temporal prediction technique proposed by our research group [1]}, [2]}, [3]} has recently been applied to the field of robotics to predict soil deformation in bucket excavation [4]}.
Such propagation of techniques from fluid mechanics motivates us that our proposed technique for highly nonlinear dynamics can be applied to a wide range of science.
Moreover, as demonstrated in the manuscript, the methods can be applied not only to fluid flows, but also for geophysical observation.
We can expect the applicability of our techniques to other types of geophysical data, e.g., weather and temperature field, as well.
We refer the enthusiastic readers to our previous paper which investigates the various parameter settings of convolutional neural network, which are utilized in all models covered in this study [5]}.
